International Business Machines Corporation

Mohammed Mostafa

Graham John Wills

Lin Luo

Changying Sun

Graham A Watts

Kim D Letkeman

Scott D Cowan

Ali Khanafer

Firas Kazem

Anthony Nicola Tasca

Ahmed Hussein Mohamed Kamel El-Khouly

Laura Marie Alkhoury

Marco Pistoia

Takaaki Tateishi

Paolina Centonze

Stephen David Gibson

Vishwanath Ramaswamy

Mohamad F Kalil

Joyce Miryam Habbouche

Frederic Plante

Joshua P Tessier

Rola Shaar

Mohamed El Gemaiey

Morvarid Sehatkar

Thomas A MacDougall

Steven J Gutz

A computer-implemented method, system and computer program product for creating a descriptor for a dashboard template. The column-to-visualization mappings are extracted from a dashboard of a created or modified dashboard (or a created or modified dashboard template). Furthermore, the concept combinations from each visualization of the dashboard are extracted. Additionally, topics from the dashboard are extracted. The concept combinations, topics and column-to-visualization mappings are aggregated into a dashboard template descriptor. The dashboard template descriptor is then stored. In this manner, the dashboard template descriptor captures how concept combinations are used in the visualizations of the dashboard as well as how high-level concepts (topics) are incorporated in the dashboard. Furthermore, the dashboard template descriptor captures how the concepts of the columns of a dataset are mapped to the visualizations of the dashboard. As a result, the most appropriate dashboard/dashboard template may be selected to visualize the user's dataset.

Creating descriptors for business analytics applications

A computer-implemented method, system and computer program product for recommending a template or a dataset. A descriptor (input descriptor) of the input, corresponding to a dataset or a dashboard template, is extracted or fetched. The input descriptor may be the descriptor of the dataset which includes topics and concept combinations. Alternatively, the input descriptor may be the descriptor of the dashboard template which includes topics, concept combinations and column-to-visualization mappings. Scores are then generated for the candidate targets, corresponding to dashboard templates or datasets, based on one or more of the following: matching topics in the input descriptor with topics in the candidate target descriptors, matching concept combinations in the input descriptor with concept combinations in the candidate target descriptors, and determining compatibility of the datasets with the dashboard templates. Dashboard template(s) or dataset(s) are then recommended based on the generated scores for the candidate targets.

Recommending template of business analytics applications based on user's dataset

Methods and systems for similarity-based value-to-column classification are disclosed. A method includes: receiving, by a computing device, a natural language search query; determining, by the computing device, a filtering phrase in the natural language search query using a natural language understanding model; encoding, by the computing device, the filtering phrase; retrieving, by the computing device, a plurality of encoded columns; for each of the plurality of encoded columns, the computing device determining a similarity score based on a similarity between the encoded filtering phrase and the encoded column; and outputting, by the computing device, a column corresponding to an encoded column of the plurality of encoded columns having a highest similarity score.

Similarity-based value-to-column classification

A computer-implemented method, system and computer program product for providing predictions tailored for a specific domain. Each field within a set of datasets is classified with a concept defined in a domain specific ontology. After receiving a target field and identifying a target concept tagged to the target field, concepts that have an influential impact directly to the target concept or indirectly to a concept tagged to a field associated with the target field are identified. Fields are then identified as candidates for predictors from the identified concepts using a semantic relationship and a numeric association combined approach. An influential confidence score and a numeric analytics score are then calculated for each of these candidates upon which a final confidence score is computed. The identified candidates are ranked based on the final confidence scores, and the top ranked candidates for predictors are selected to build a statistical model.

Providing predictive analytics with predictions tailored for a specific domain

Guided exploration of data is provided. A semantic graph corresponding to a dataset is generated using identified relations among columns of the dataset that are identified based on mapping the columns to main concepts in a generic ontology. A subgraph of the semantic graph is formed based on identification of nodes corresponding to relevant central concepts within the semantic graph using graph centrality metrics. A plurality of paths is identified in the subgraph using a bi-directional multiple hop search from the nodes corresponding to the relevant central concepts in the subgraph. A relevance score is assigned to each path in the plurality of paths using a graph-theoretic metric and the graph centrality metrics. A set of natural language questions based on relevant central concepts and concept relations corresponding to nodes in each respective path with an assigned relevance score greater than a minimum threshold score is output to the user.

Dynamic natural question generation via semantic knowledge representation

An approach is provided in which the approach performs object detection on an image to identify a set of edges and a set of tables from a set of objects corresponding to a set of data. The approach determines a set of associations between the set of tables and the set of edges in response to adjusting a set of bounding boxes corresponding to the set of edges. The approach generates a data module based on the set of associations, the set of tables, and the set of edges, and utilizes the data module to access the set of data.

Data module creation from images

A computer-implemented method for joining data sets with mismatched geographic location naming conventions is provided. The method includes identifying, by the computer, a first dataset and a second dataset as join candidates. The method also includes joining, by the computer, the first dataset and the second dataset when, each row of the first user dataset is associated with a single geographic identifier using a geographic knowledge dataset that includes a geographic name lookup table and each row of the second user dataset is associated with a single geographic identifier using the geographic knowledge dataset, wherein the geographic name lookup table includes a plurality of alias names for each of a plurality of unique geographic locations.

Method to automatically join datasets with different geographic location naming conventions

A big data analytics system includes an event monitoring controller that detects a user targeted event from an initial digital query data flow. An event conversion controller determines at least one contextual element associated with the at least one user targeted event, and automatically converts the at least one contextual element into a search criteria. An external data query builder controller automatically executes an external search query based on the search criteria. A data fusing controller automatically fuses the at least one contextual element generated from the at least one user targeted event, together with external search results generated from the external search query and the initial query data flow to generate a fused data file.

Automatically connecting external data to business analytics process

Techniques are described for selecting, based on a data visualization intent specification and a defined set of data fields associated with a set of data, two or more Meta templates that meet the data visualization intent specification and that support the set of data fields, for determining chart parts that can be used within the selected Meta templates to form chart templates and for determining, based on a set of combination rules and the specification, the chart templates that meet the data visualization intent specification.

System and method for visualizing data

The present invention discloses a solution for adding a profiling agent to a virtual machine (VM), which permits unit test programs to access information relating to memory and performance from runtime data areas of the VM. Operations permitted by the agent can include starting/stopping VM monitor, getting objects from the heap, retrieving an invocation count, determining a size of an instantiated object, getting a current thread time, and the like. Memory and performance information gathered during a test can be stored for later analysis under a test version identifier. A comparison engine can create reports that use the stored performance and memory data, which can help developers compare memory/performance deltas among different unit versions. In one embodiment, the VM can be a JAVA VIRTUAL MACHINE (JVM) and the unit test programs can be JUNIT programs.

Adding a profiling agent to a virtual machine to permit performance and memory consumption analysis within unit tests

Embodiments of the present invention address deficiencies of the art in respect to merging artifacts in a version control system and provide a novel and non-obvious method, system and computer program product for compositing deltas when merging artifacts in a version control system. In one embodiment, a method for compositing deltas for artifacts can be provided. The method can include generating deltas for a contributor artifact of an ancestor artifact, identifying interrelated ones of the deltas and grouping the interrelated ones of the deltas into a composited set of deltas. The method further can include rendering the composited set of deltas in a hierarchical view of a compare view for a version control data processing system in a development platform.

Compositing deltas when merging artifacts in a version control system

An example method includes receiving a first input indicating user activity at a first position in a document in a document editing application. The document editing application is navigable among different partial views of the document. The document editing application displays a frame element that indicates a position of a presently displayed partial view of the document is with reference to an entirety of the document. The method further includes displaying, in response to the first input indicating user activity, a first marker, in or proximate to the frame element, indicating where the first position of user activity is located with reference to the entirety of the document.

Transient, real-time position tracking interface for user activity in a document editing application

The present disclosure includes techniques for storing indexed values that may increase computing performance. In one example, a method includes receiving a request to store an input value in a data structure. The method also includes determining the input value is not represented by an index value of the first group of index values, generating an index value that represents the input value, and determining the generated index value is not included in the first group of representations. The method also includes generating a second group of index values wherein a first size of index values of the first group is different than a second size of index values of the second group. The method also includes modifying a storage capacity of the data structure to store the index values of the second group of the second size and storing the second group of index values in the data structure.

Growing community of inventors

Kanata, Canada

Mohammed Mostafa